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A Comprehensive Report on Emergency Department(ER) Operations

This comprehensive report examines emergency department (ER) operations, focusing on critical key performance indicators (KPIs) that reflect the efficiency and effectiveness of patient care from triage to treatment.It analyzes patient visit metrics to measure overall demand and examines the time from door to doctor, providing insights into initial response times. The report also looks into the duration from doctor to decision, highlighting the speed at which healthcare providers can evaluate and decide on necessary interventions. For more information visitER Deparment

Author: Joan Jimenez

Date: October 10, 2024

Introduction

Quarto is probably my favorite tool in the R/Python universe. Transforming your code and thoughts into a stunning report in one click always blows my mind.

While the default Quarto appearance looks great, there are a few tweaks I always apply to elevate the report even further. Rather than manually adding them each time, I bundled everything into a custom format called lumo.

Btw, you can learn how to master Quarto thanks to my online course: Productive R Workflow

This document aims at showcasing how a quarto document made with the lumo format looks like.

Load Packages

Make sure to load all your packages at the start of your document for clarity and consistency.

Notice that the code chunks are folded by default. You can adjust this behavior with the code-fold option in the document’s YAML header.

  1. this package provides my favorite ggplot2 theme: theme_ipsum()
  2. because I was too lazy to find something better in the R Color Finder!

By the way, you should open the code chunk that is folded above. ⬆️ There is a little stunning surprise on its right hand side.

Main Features

Typography

You may have noticed the typography differs from the default Quarto style. I’ve also added extra spacing above each title for better readability, and underlined them to make the headings stand out more.

Header & Footer

The header is fully customized, complete with a fun GitHub corner linking to the original code. Plus, there are animated particles for a touch of whimsy. Scroll down, and you’ll find a custom footer as well.

For a complete list of features, visit the documentation!

Oh and since we’re here, did you know you can easily include a full width interactive map with leaflet in a quarto doc? 😍

How It Works

Lumo is a Quarto custom format. With a single command, you can add an _extension folder to your working directory, containing all the styles and content needed to create a report like this.

It takes less than 28 seconds, and you can find all the details here.

By the way, did you know you can link graphs together using ggiraph? Its syntax is quite similar to ggplot2 and it produces stunning, interactive outputs. Try hovering over one of the charts below!

Pretty close to magic, isn’t it?

Code 
# Read the full world map
world_sf <- read_sf("https://raw.githubusercontent.com/holtzy/R-graph-gallery/master/DATA/world.geojson")
world_sf <- world_sf %>%
  filter(!name %in% c("Antarctica", "Greenland"))

# Create a sample dataset
happiness_data <- data.frame(
  Country = c(
    "France", "Germany", "United Kingdom",
    "Japan", "China", "Vietnam",
    "United States of America", "Canada", "Mexico"
  ),
  Continent = c(
    "Europe", "Europe", "Europe",
    "Asia", "Asia", "Asia",
    "North America", "North America", "North America"
  ),
  Happiness_Score = rnorm(mean = 30, sd = 20, n = 9),
  GDP_per_capita = rnorm(mean = 30, sd = 20, n = 9),
  Social_support = rnorm(mean = 30, sd = 20, n = 9),
  Healthy_life_expectancy = rnorm(mean = 30, sd = 20, n = 9)
)

# Join the happiness data with the full world map
world_sf <- world_sf %>%
  left_join(happiness_data, by = c("name" = "Country"))


# Create the first chart (Scatter plot)
p1 <- ggplot(world_sf, aes(
  GDP_per_capita,
  Happiness_Score,
  tooltip = name,
  data_id = Continent,
  color = Continent
)) +
  geom_point_interactive(data = filter(world_sf, !is.na(Happiness_Score)), size = 4) +
  theme_minimal() +
  theme(
    axis.title.x = element_blank(),
    axis.title.y = element_blank(),
    legend.position = "none"
  )

# Create the second chart (Bar plot)
p2 <- ggplot(world_sf, aes(
  x = reorder(name, Happiness_Score),
  y = Happiness_Score,
  tooltip = name,
  data_id = Continent,
  fill = Continent
)) +
  geom_col_interactive(data = filter(world_sf, !is.na(Happiness_Score))) +
  coord_flip() +
  theme_minimal() +
  theme(
    axis.title.x = element_blank(),
    axis.title.y = element_blank(),
    legend.position = "none"
  )

# Create the third chart (choropleth)
p3 <- ggplot() +
  geom_sf(data = world_sf, fill = "lightgrey", color = "lightgrey") +
  geom_sf_interactive(
    data = filter(world_sf, !is.na(Happiness_Score)),
    aes(fill = Continent, tooltip = name, data_id = Continent)
  ) +
  coord_sf(crs = st_crs(3857)) +
  theme_void() +
  theme(
    axis.title.x = element_blank(),
    axis.title.y = element_blank(),
    legend.position = "none"
  )

# Combine the plots
combined_plot <- (p1 + p2) / p3 + plot_layout(heights = c(1, 2))

# Create the interactive plot
interactive_plot <- girafe(ggobj = combined_plot)
interactive_plot <- girafe_options(
  interactive_plot,
  opts_hover(css = "fill:red;stroke:black;")
)

interactive_plot

Figure: three graphs connected thanks to ggiraph

Interactive table

It is very easy to insert an interactive table in your document thanks to the DT package. The output allows to filter rows, search for something and sort using a specific columns!

Install the library with install.packages("DT"). Then, just pass a dataframe to the datatable() function to get a stunning interactive output!

Code 
library(DT)
data(iris)

# Make a table
datatable(iris, filter = "top")







I’ve added a bit of CSS to make the font smaller on DT tables by the way 🙂

A grey section

It’s always good to have a grey section. Makes the document breath a bit. I’ve added a little utility class in Lumo to make sure you can make sections like this very easily. Check the documentation!

Let’s use this space to render a little equation:

\[ x = \frac{-b \pm \sqrt{b^2 - 4ac}}{2a} \]

Quarto Tricks

Lumo is a great format, but to get the most out of it, you’ll need to master a few Quarto tricks!

The good news? I’ve compiled my 30+ favorite tips in one place. Just spend 30 minutes, and you’ll be up to speed with all of them!

For example, if you’re creating multiple plots, each highlighting different aspects of your dataset, consider using pills to organize them!

This is a good old scatterplot of the iris dataset. If you want to learn more about how to make graphs in R, visit my R graph gallery!

Code 
ggplot(iris, aes(x=Sepal.Length, y=Sepal.Width, color=Species)) + 
    geom_point() +
    theme_ipsum()

You can also make a boxplot, but please do not hide the underlying distribution! This is widely explain in my data-to-viz.com project in case you’re not convinced 😋. Check the next tab to get convinced!

Code 
# create a dataset
data <- data.frame(
  name=c( rep("A",500), rep("B",500), rep("B",500), rep("C",20), rep('D', 100)  ),
  value=c( rnorm(500, 10, 5), rnorm(500, 13, 1), rnorm(500, 18, 1), rnorm(20, 25, 4), rnorm(100, 12, 1) )
)

# Plot
data %>%
  ggplot( aes(x=name, y=value, fill=name)) +
    geom_boxplot() +
    scale_fill_viridis(discrete = TRUE, alpha=0.6) +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=11)
    ) +
    ggtitle("A boxplot that hides the underlying distribution") +
    xlab("")

See what’s happening now that the individual data points are visible! Would be a shame to miss this.

Code 
data %>%
  ggplot( aes(x=name, y=value, fill=name)) +
    geom_boxplot() +
    scale_fill_viridis(discrete = TRUE, alpha=0.6) +
    geom_jitter(color="black", size=0.4, alpha=0.9) +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=11)
    ) +
    ggtitle("A boxplot with jitter") +
    xlab("")

More Information

Learn how to use and how to customize Lumo here.

Session Info

When creating a Quarto document, it’s a good practice to include details about your working environment.

This is easy to do—just call sessionInfo(), and it will print out all your R and package versions. This simple step greatly enhances reproducibility.

Code 
sessionInfo()
R version 4.4.0 (2024-04-24)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.5 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0

locale:
 [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
 [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
 [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
[10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   

time zone: UTC
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] DT_0.33           sf_1.0-17         patchwork_1.3.0   ggiraph_0.8.10   
 [5] viridis_0.6.5     viridisLite_0.4.2 hrbrthemes_0.8.7  lubridate_1.9.3  
 [9] forcats_1.0.0     stringr_1.5.1     dplyr_1.1.4       purrr_1.0.2      
[13] readr_2.1.5       tidyr_1.3.1       tibble_3.2.1      ggplot2_3.5.1    
[17] tidyverse_2.0.0  

loaded via a namespace (and not attached):
 [1] gtable_0.3.5            bslib_0.8.0             xfun_0.48              
 [4] htmlwidgets_1.6.4       tzdb_0.4.0              crosstalk_1.2.1        
 [7] vctrs_0.6.5             tools_4.4.0             generics_0.1.3         
[10] proxy_0.4-27            fansi_1.0.6             pkgconfig_2.0.3        
[13] KernSmooth_2.23-24      uuid_1.2-1              lifecycle_1.0.4        
[16] compiler_4.4.0          farver_2.1.2            munsell_0.5.1          
[19] fontquiver_0.2.1        fontLiberation_0.1.0    sass_0.4.9             
[22] htmltools_0.5.8.1       class_7.3-22            yaml_2.3.10            
[25] Rttf2pt1_1.3.12         jquerylib_0.1.4         pillar_1.9.0           
[28] extrafontdb_1.0         classInt_0.4-10         cachem_1.1.0           
[31] fontBitstreamVera_0.1.1 tidyselect_1.2.1        digest_0.6.37          
[34] stringi_1.8.4           labeling_0.4.3          extrafont_0.19         
[37] fastmap_1.2.0           grid_4.4.0              colorspace_2.1-1       
[40] cli_3.6.3               magrittr_2.0.3          utf8_1.2.4             
[43] e1071_1.7-16            withr_3.0.1             gdtools_0.4.0          
[46] scales_1.3.0            timechange_0.3.0        rmarkdown_2.28         
[49] gridExtra_2.3           hms_1.1.3               evaluate_1.0.0         
[52] knitr_1.48              rlang_1.1.4             Rcpp_1.0.13            
[55] glue_1.8.0              DBI_1.2.3               renv_1.0.10            
[58] jsonlite_1.8.9          R6_2.5.1                systemfonts_1.1.0      
[61] units_0.8-5

This document was made by the Maryland Department of Transportation, based on the Lumo quarto template.